Capacity Constraints and Cost-Effectiveness: A Discrete Event Simulation for Drug-Eluting Stents

Jahn, Beate; Pfeiffer, Karl P.; Theurl, Engelbert; Tarride, Jean‐Éric; Goeree, Ron

doi:10.1177/0272989x09336075

Cited by 22 publications

(36 citation statements)

References 22 publications

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“…RM performed at an aggregate level). Out of the 10 articles, 5 studies applied RM at an organisational level [19][20][21][22][23]] and 5 at a national level [24,[26][27][28][29].…”

Section: Searches and Siftingmentioning

confidence: 99%

“…Regarding the type of analyses, out of the 10 articles identified in the systematic review, only 1 article [21] includes RM aspects (i.e. resource constraints) in both CEA and BIA models, while the rest of the articles focus solely on CEA.…”

Section: Searches and Siftingmentioning

confidence: 99%

“…The outcomes that were observed in these studies included total flow time [19,20,[27][28][29], waiting time [19][20][21][22][23][24][26][27][28][29], waiting probability [28], patient throughput [19-21, 23, 27, 28], resource utilisation [19-21, 23, 26, 28], queue length [21,23,24,28,29], probability of abandonment [28], and service level [28]. In one study observed [22], only one RM outcome was mentioned (waiting time); the remainder of these studies reported multiple outcomes.…”

Section: Searches and Siftingmentioning

confidence: 99%

“…However, in some studies this is not the case, the capacity constraints are not modelled overtly but rather at a higher level of abstraction indirectly using throughput measures. For example, one study [21] limited the throughput to thirty-six patients per day as a result of the constraints in the availability of resources (e.g. doctors, specialist nurses) to meet daily demand.…”

Section: Searches and Siftingmentioning

confidence: 99%

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Discrete Event Simulation-Based Resource Modelling in Health Technology Assessment

et al. 2017

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OBJECTIVE:To conduct a systematic review of published research on the use of discrete event simulation (DES) for resource modelling in health technology assessment (HTA). Resource modelling (RM) is broadly defined as incorporating and measuring effects of constraints on physical resources (e.g. beds, doctors, nurses) in HTA models. METHODS: Systematic literature searches were conducted in academic databases (JSTOR, SAGE, SPRINGER, SCOPUS, IEEE, Science Direct, PUBMED, EMBASE) and grey literature (Google Scholar, NHS journal library), enhanced by manual searchers (i.e. reference list checking, citation searching and hand searching techniques). RESULTS:The search strategy yielded 4,117 potentially relevant citations. Following the screening and manual searches, 10 articles were included. Reviewing these articles provided insights into the applications of RM: firstly, different types of economic analyses, model settings, RM and costeffectiveness analysis (CEA) outcomes were identified. Secondly, variation in the characteristics of the constraints such as types and nature of constraints, sources of data for the constraints were identified. Thirdly, it was found that including the effects of constraints caused the CEA results to change in these articles. CONCLUSION:The review found that DES proved to be an effective technique for RM but there were only a small number of studies applied in HTA. However, these studies showed the important consequences of modelling physical constraints and point to the need for a framework to be developed to guide future applications of this approach. (230 words) Keywords Discrete event simulation, resource modelling, health technology assessment, simulation modelling 2 Key Points for Decision Makers  Economic evaluation studies in health care typically ignore the short-term constraints on physical resources (e.g. doctors, nurses) which can lead to incorrect results.  Discrete Event Simulation is an effective tool for modelling the effects of constraints but there were only a small number of studies applied in health technology assessment (HTA).  Further research is required to examine the possible developments for detailed modelling of the resource constraints in HTA.

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“…RM performed at an aggregate level). Out of the 10 articles, 5 studies applied RM at an organisational level [19][20][21][22][23]] and 5 at a national level [24,[26][27][28][29].…”

Section: Searches and Siftingmentioning

confidence: 99%

Section: Searches and Siftingmentioning

confidence: 99%

Section: Searches and Siftingmentioning

confidence: 99%

Section: Searches and Siftingmentioning

confidence: 99%

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Discrete Event Simulation-Based Resource Modelling in Health Technology Assessment

et al. 2017

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“…Jahn et al [34] showed in a case example for cost effectiveness of drug-eluting stents that neglected limited capacities can cause wrong results. They developed a DES model where resources capacities and dynamic waiting lines were explicitly modelled.…”

Section: Examples Of Resource Modelling At Individual Organisational mentioning

confidence: 99%

Resource Modelling: The Missing Piece of the HTA Jigsaw?

Thokala

Dixon

Jahn³

2014

PharmacoEconomics

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AcknowledgementsThe authors would like to acknowledge the feedback from the reviewers. There are no conflicts of interest for any of the authors. SD wrote the initial drafts for sections 1, 2 and 6. PT and BJ wrote the initial drafts for sections 3, 4 and 5. All authors contributed to the finalising the paper and revising the paper based on reviewers' comments. PT is the overall guarantor for the paper. 2 Abstract:Within health technology assessment (HTA), cost-effectiveness analysis and budget impact analyses have been broadly accepted as important components of decision making. However, whilst they address efficiency and affordability, the issue of implementation and feasibility has been largely ignored. HTA commonly takes place within a deliberative framework that captures issues of implementation and feasibility in a qualitative manner.We argue that only through a formal, quantitative assessment of resource constraints can these issues be fully addressed. This paper argues the need for resource modelling to be considered explicitly in health technology assessment (HTA). First, economic evaluation and budget impact models are described along with their limitations in evaluating feasibility. Next, resource modelling is defined and its usefulness is described along with examples of resource modelling from the literature. Then, the important issues that need to be considered when undertaking resource modelling are described before setting out recommendations for the use of resource modelling in HTA. Key Points for Decision Makers• Economic analyses typically ignore the short term constraints (e.g. beds, availability of CT scanners, nurses, etc) which might lead to low levels of uptake.• A quantitative assessment of technology diffusion, its related resource requirements and capacity constraints is required for uptake to be formally considered by decision makers • Resource modelling is especially useful if there are significant changes in the amount or type of resources needed within the pathway by implementing the new technology • Modelling techniques exist that can capture these resource implications and these analyses can be performed at a national level or at an organisational level 3

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On the role of data, statistics and decisions in a pandemic

Jahn¹,

Friedrich

Behnke

et al. 2022

AStA Adv Stat Anal

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A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings.

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Capacity Constraints and Cost-Effectiveness: A Discrete Event Simulation for Drug-Eluting Stents

Abstract: Our model shows that neglected limited capacities can cause wrong cost-effectiveness results. Therefore, capacities should be explicitly included in decision-analytic models if there is evidence of scarcity.

Cited by 22 publications

References 22 publications

Discrete Event Simulation-Based Resource Modelling in Health Technology Assessment

Discrete Event Simulation-Based Resource Modelling in Health Technology Assessment

Resource Modelling: The Missing Piece of the HTA Jigsaw?

On the role of data, statistics and decisions in a pandemic

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